Creating Apps & Python

Digital transformation

)In this article I will also share the thoughts of a handful experts, who were willing to be interviewed by Lisa de Kluijver and me and contributed with their experience. 

A special thanks to:

- Lieke Melsen – Assistant Professor Computation Hydrology at Wageningen University
- Thijs Damsma – Data Engineer at Van Oord
- Jacco Hoekstra – Professor CNS/ATM (Communication, Surveillance, Navigation / Air Traffic Management), and former Dean of the faculty of Aerospace Engineering, at Technical University Delft
- Johan Tuls – Structural Engineer at BAM Infraconsult
- Philip Vollet – Senior Data Engineer at KPMG and [Machine Learning](https://www.viktor.ai/blog/40/artificial-intelligence-machine-learning-engineering-construction) and Natural Language Processing (NLP) Specialist
- Maaike van Putten – IT trainer and developer at Bright Boost

This is part of a series of articles about engineering and Python. Each time we will go deeper into engineering content and talk about solving specific problems. Other articles on this subject are:

- [How are engineerings creating professional software tools?](https://www.viktor.ai/blog/18/digital-transformation-python-BIM-data-analysis--artificial-intelligence)
- [Parametric design in the cloud with Grasshopper](https://www.viktor.ai/blog/13/parametric-design-python-grasshopper-excel)
- [Automating RFEM model generation with Python in a web application](https://www.viktor.ai/blog/9/web-application-automation-python-RFEM)
- [How to make an IDEA StatiCa Concrete model with Python](https://www.viktor.ai/blog/8/web-application-python-IDEA-StatiCa-Concrete)
- [Combine the power of Dynamo and Python in the cloud for easy sharing and accessibility](https://www.viktor.ai/blog/32/parametric-design-web-application-python-dynamo-real-estate)
- [How to integrate a PLAXIS model into a cloud-based parametric engineering tool with a few lines of Python code](https://www.viktor.ai/blog/34/integration-plaxis-python-parametricdesign) 
- [Automated Pile Driveability analysis with GRLWEAP and Python](https://www.viktor.ai/blog/22/parametric-design-python-GRLWEAP-pile-driveability)
- [Using Python to create parametric SCIA models in the cloud](https://www.viktor.ai/blog/10/parametric-design-python-SCIA)
- [An application with custom Python scripts to save time and reduce errors](https://www.viktor.ai/blog/27/web-application-python-D-foundations-Excel)

## Boosting your digital transformation

### The frustration of the iteration
Eight years ago, I graduated from Aerospace Engineering at the University of Technology Delft. During my study I used all kinds of parametric design models, advanced analysis, and optimization techniques. When I started working as a Structural Engineer, I was disappointed: there was never time, budget, and willingness to create professional software tools that could make a big difference in the long term.

Frustrated by the large amount of boring and repetitive tasks of a typical engineering project, I went on making software tools myself, using project hours. This was a little risk-taking and it felt a bit clandestine. I was supposed to just update the models and reports, but it seemed that no one cared as long I was meeting the deadlines. I never regret this decision, as those tools enabled me to react more quickly to the many design changes in a project every time.

### My Python toolbox

It took me a while before I discovered the best programming language to do this. At university, I learned to use Matlab; however, not every engineering company I worked for could afford those expensive licenses. Frustrated by the limitation of my Excel sheets I decided to give it a boost by learning Visual Basic (VBA). What a bad choice. I wish that someone had told me: use Python!

With time, I had a small library with codes. It was like having a box with digital building blocks that I could connect and combine to solve a specific problem, making it easier and quicker to make those programs each time. The advantages of having a software library were obvious, but the question remaind: Why were the companies for whom I was working not building their own libraries?

### Going beyond the project system

It took me some time to understand. Many companies work project-based, and that was exactly the problem. Everything needs to be done between the scope, budget, and limitations of a project. This project-based model does not stimulate creating software as a company, that can later be used in several other projects as well. Due the lack of good software, design changes need to be done individually by each discipline, models are updated manually, and there is a lot of (slow) communication. Therefore, each change has a significant impact on the progress. I call this the sawtooth effect (see figure).

![Graph of the sawtooth effect](/uploads/The_sawtooth_effect_92f0e4b302.png)

### Building digital assets

Starting to build custom software tools as a company requires a change in mentality. It is investing upfront and benefiting later, which may seem risky. Making a proper software tool from scratch is risky as well and can require a huge effort at the beginning of the project. However, when done properly, progress can go exponentially and calculating and reporting a design change can become a matter of minutes (see figure).

At the beginning, progress is hard to track, which makes people nervous. I think this is a big reason why many companies are not taking the first step to creating custom software for their projects. The solution is therefore investing in digital building blocks. Once you have a few of those in your possesion,  you can reutilize and connect them to quickly make software applications and benefit immediately. It is clear: Digital building blocks are assets for the company, that everyone can use, and provide a long-term benefit.

> "Construction companies grow linearly, but programming their own building blocks makes it exponential. Programming building blocks is like building up capital. Each time you can make more, better, quicker and cheaper software applications, allowing you to make a big step forward." - Johan Tuls, BAM


## Some background info about Python

Python was created by Guido van Rossum and first released in the beginning of the nineties. The aim was to help programmers write clear, logical code for both small and large-scale projects. That is exactly the reason Python has grown as big as it is now.

> "Python is the future because it is a lingua franca. It transcends all platforms and domains. You can 
use it for infinitely different software applications. This makes Python unique." - Jacco Hoekstra, Technical University Delft

![Graph_Popularity of Programming Language.svg](https://cms.viktor.ai/uploads/Graph_Popularity_of_Programming_Language_af37efc9c6.svg)


To sum it up, these are the most important reasons for why Python has become so popular:

- **It is free and easy to learn!** Out of all programming languages it is probably the one that resembles English most, meaning you can go from thought to code very fast and intuitive.
- **It is not only about the language.** It is also an amazing open and accessible world of knowledge and available resources.
- **You can do anything with Python**, from simple maths to scientific calculations on a supercomputer, including Data Science, Machine Learning, creating web-based applications, and much more.
- **It is very scalable** and suitable for working together with other people, even from different disciplines. 

With Python, you can start small and slowly build up a digital empire. Do you not believe me? Google, Facebook, Instagram, Spotify, and Netflix; they all use Python as well! Just Google it ;).

## Digital Building Blocks
I noticed that the common approach to build scalable software and cooperate with other specialists is literally thinking in digital building blocks that can be connected to each other. This make the process versatile and scalable. 

Some benefits of using this building block approach are:

- You can build code fast by utilizing someone else's work! No starting from scratch.
- It enables people to share knowledge in the form of code!
- It makes it easier to replace parts of your code!
- It makes growing the code more controllable, as each block has a specific function!

This approach started in the software development community, with object-oriented programming and packages you could use to make your own software tools. Nowadays, it is becoming more popular in Engineering, Data Science, Architecture, and the Scientific Community as well.

Anything you can connect and command can be seen as a building block. It could be from a simple script, from one of the many free Python packages, or from FEM software.

## Objects: The building blocks of Python

Python is object-oriented. This allows you to break down your software into small problems (we call them objects) that you an then solve seperately, one object at a time. It is like building something large out of smaller components. This is a good match since it is very similar to how I, as an engineer, look at physical objects that have to be designed, built and maintained.

### Object creation

To create an object, you need to define a class. A class is a template for creating objects on which you are able to define its' behaviour and properties. A class can be used to make as many objects as you need. The great thing is that you can use properties of different objects for other calculations or for creating a ‘larger’ object as well. For example, you can create many beam objects, and then use their mass and stiffness properties to calculate the deflection in the bridge object.

### Free information flow

Information between objects can flow very freely. This is totally different from scripts typically made when using Visual Basic, Matlab or FEM packages. Here, the code runs top-down, meaning that changing something in the code will affect everything beneath it. This is like playing Jenga. The more complex it gets, the greater the chances become that it will collapse. With Python however, this problem does not exist.

In general, you can see objects as digital building blocks for making a larger software program. Allowing different specialists to build, maintain and improve their objects, and share it with the rest of the world to make something even more awesome!

## Free building blocks
The Python Package Index (PyPI) has 243,692 packages you can use for free! Most are not self-standing software, but building blocks for creating larger software tools. There are packages for visualizations, Machine Learning, Data Science, numerical calculus, and much more. Their quality is very high, so use them well and build your code fast! 

For example: 
- Numpy can be used to do all kind of matrix operations. Its functionalities are comparable to MATLAB. 
- Pandas helps you manipulate and analyse large sets of data without having to learn a specialized data-processing language. 
- PyTorch enables you to use machine learning without being a specialist. 

Which ones do you use the most?

> "There is a democratization of knowledge. It has become easier to use people’s tools and get insight into other disciplines thanks to the many available software packages." - Thijs Damsma, Van Oord

## External software is just another block

Quite often I hear that engineers are facing the limits of the standard software packages they use and this is leading to cause a change in mentality. Nowadays, standard software packages are not considered to be the total solution anymore. These software packages are just part of a greater process and engineers are now searching for connectivity.

These days, a large number of external software packages can also be commanded with Python. This is very powerful, as it enables you to use them like any other digital building block in your code. For example, you can combine a cost model made in Excel with a structural calculation to find the cheapest solution that satisfies the structural requirements. This can also be done with other tools, such as Grasshopper, Dynamo, SCIA, Plaxis, Diana, Excel, ANSYS and Deltares D-Series.

![External software packages that can be commanded with Python](/uploads/External_software_packages_that_can_be_commanded_with_Python_f270df6020.png)

## What’s next?

### Knowledge distribution
As an engineering community we still have a great challenge: the distribution of knowledge. What do you think is the best way of distributing digital building blocks and making knowledge available to others? I would like to hear your opinion about this topic, so don't hesitate to contact me!

Personally, I think there are two groups: the programmer, who can read, understand, and use a code; and the end-user who cannot program, but is going to use the tool. 

For the first group, good distribution channels are still missing. As a result, many of these specialists are programming the same theories or creating programs for solving the same problems over and over again.

> "We lose enormous amounts of time by programming the same models. If there was a central place where these models where available, we could invest that time in going forward."  - Lieke Melsen, Wageningen University

### Marketplaces for software applications

I believe soon there will be marketplaces where specialist can offer the software applications they made, so they can be used as digital building blocks for solving larger or more specific problems. There should be a financial compensation, such that companies and freelancers see this as an investment, and the creation of specific engineering tools is stimulated. If you are interested in this marketplace, let me know as well!

### Additional challenges

For the second group, the ones that cannot read or write code, another solution must be found. I am a strong believer in user interfaces and online software applications. 

> “A big benefit of web-based applications is that the different disciplines have access to a common environment and always can make decisions based on the latest version of the information.” - Philip Vollet, KPMG

## Want to know more?

This is the first of a series of articles about engineering and Python. We will talk about how you can make professional engineering applications that everyone can use, as well as on how you can make [parametric design tools](https://www.viktor.ai/blog/46/parametric-design-cloud-engineering-construction) using Python and common engineering software like Grasshopper, Dynamo, SCIA, RFEM and IDEA. To write these articles we interviewed engineers that make these kinds of tools on a daily base. Real pros!

**Stay tuned! Follow us on LinkedIn, so you don't miss anything.**

Maybe you wonder why we are writing this. VIKTOR is on a mission to unleash the world’s real engineering potential. We are developing a platform where engineers can build their own web-based applications using Python. We work closely together with engineering, construction and manufacturing companies and help them boost their digital transformation. We would love to hear your feedback on our ideas, try out the platform, and help us create the perfect tools that enables you to:

AUTOMATE THE BORING. ENGINEER THE AWESOME!

If you are interested in [developing your own parametric design tools](https://www.viktor.ai/parametric-design-software), make sure to [check out our solutions page](https://www.viktor.ai/parametric-design-software).


Accelerate your digital transformation using Python

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1. What is parametric design, and how is it evolving
2. Collaborative parametric design: working together for better results
3. How to make tools available to others
4. Active collaboration on parametric design tools
5. Parametric design in a billion-euro project
6. What collaborative parametric design means for you



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The adoption of parametric design grows exponentially thanks to friendly programming languages like Python or visual programming packages like Dynamo or Grasshopper.
 
Our perception is changing. We no longer see parametric design as a model to solve specific technical problems, but rather as a new way of working, in which stakeholders collaborate to find the best solution for a project.

Learn about the latest trends and the future of parametric design in this white paper

Collaborative parametric design models allow you to work together for better solutions

Download the white paper and get INSPIRED

Did you know parametric design models are no longer just used to solve specific technical problems, but rather as a new way of working? Read here how stakeholders use parametric design to collaborate and find the best solutions for their projects!

The Future of Parametric Design is COLLABORATION

## Pile foundation design with D-Foundations

Most companies that are involved in designing and building pile foundations need to perform many analyses to ensure that their foundation designs meet the safety standards as described in the NEN. Commonly, D-Foundations software is used to perform these calculations. 

D-Foundations is a software-package, created by Deltares: An academic institute that focuses on Hydro- and Geotechnologies. It is mostly used in the Civil and Architecture sectors. With D-Foundations, you can calculate the required depth of a set of specified foundation piles, placed in a particular soil composition (based on GEF files) and under a certain load. Using location-specific soil information, these kind of foundation calculations can be applied to many different constructions in the Building and Infrastructure sector, such as a factory floor for heavy machinery, a tall apartment building, or even the foundations for wind turbines and high voltage pylons. 

![D-foundations software package.jpg](/uploads/D_foundations_software_package_426dca58f8.jpg)

## Analyzing, analyzing, analyzing

Normally, to find the most optimal and cost-effective design, a Geotechnical Engineer needs to analyse a great number of possible designs. This is a time-intensive and tedious process, which is very prone to error as well. With today’s high demands on short delivery times, there is only time to evaluate a handful of designs. Because of that, the best design is not always found within that time frame.

## More analyses in less time 

To be able to run more D-Foundations analyses in shorter time, a binding between the D-Foundations software and the VIKTOR platform is made. The working principle of this binding will be explained in more detail later on in this article. 

With this binding, a D-Foundations analysis can be ran with just a few lines of Python code (see example code below). We can take the necessary input data, which is already centralized in the VIKTOR application upon uploading, and run multiple different D-Foundations analyses, without any human interaction needed. This means: No need for opening the GUI of D-Foundations and going through each of the steps manually!

![Blog_automate_processing_with_D-foundations_3](https://cms.viktor.ai/uploads/D_foundations_analysis_ran_in_few_lines_of_code_97269b2df3.png)


## Using D-Foundations

Similar to other software packages of the Geo-technical suite of Deltares, D-Foundations uses a set of input, settings, and dump files to store the in- and- output data of the calculations. These input files can be analyzed by D-Foundations in batch-mode through the command line, which will then create a dump-file with the results of the analysis. These files, however, are not very easy to understand and even harder to write yourself.

Therefore, the following automated workflow was devised:

1. Generate a correct D-Foundations input file using Python
2. Send this file to the server where D-Foundations is installed*
3. Run a D-Foundations analysis on the input file and send the data dump-file back to the application*
4. Visualize or output the data where necessary

*This requires no interaction from the user, nor does it require any coding from a developer, as this functionality is build into the VIKTOR platform

Points 2 to 4 are quite generic, so the focus of this article will be on the first point: Generating the input file. 

## Generating the D-Foundations input file

The combination of the filled-in VIKTOR web interface and the soil information that has been uploaded to the platform database hold all the information needed to perform a D-Foundations calculation. It is a matter of compiling all this data in the correct format into a D-Foundations input file. While the general concept of compiling a text-based input file is quite easy, the hardest part of creating a valid input file was the reverse-engineering of all potential input options and finding which specific pieces of information needed to be put where. 

The current average D-Foundations input file that we work with can easily contain over 100,000 lines of text. Therefore, putting the right information in the right place is essential. Unfortunately, no detailed documentation of the input file layout is available, so this process took significant time to complete. An added complexity was that the layout of the input file would change significantly, depending on which options were selected. This required numerous edge-cases to be evaluated and a lot of logic to be implemented.

In order to ensure the input file is correctly generated, the following steps have been executed:

1. GEF files (containing soil information) are analyzed and corresponding soil layouts are defined
2. The properties of the selected pile type (selected by the user) are extracted from the database
3. The grid of piles is defined, which includes the pile's locations, height, properties and loads 
4. Any additional calculation options, selected by the user in the web interface, are collected
5. All data collected in steps 1 to 4 are inserted into a generic template (.foi file), which is then rendered as the final input file

The innovative part is making the binding generic enough in such a way that it can be applied over a wide range of different projects/designs. This makes the binding future proof and much more valuable to current and future customers. 

## Ways to use the D-Foundations file

Once the D-Foundations file has been created, it can be used in several ways:  

1. The user could download the generated input file, using a download-button in the VIKTOR application. This file could then be imported into D-Foundations manually in order to visually inspect the D-Foundations model and review the report generated by D-Foundations. 
2. The user could also have the VIKTOR application run the D-Foundations analysis on an external server, after which the results are send back to the application and can be output to the screen. This way, the user can quickly analyse a design without ever having to open the D-Foundations software package.

Both these options are generic functions that are part of the generic VIKTOR platform and are therefore available but not limited to the D-Foundations software package.

![Downloading D-foundations input files in a zip-file](/uploads/Downloading_D_foundations_input_files_in_a_zip_file_27781ea934.png)
*Download D-Foundations input files in a zip-file*

![VIKTOR interface for automation in D-foundations](https://cms.viktor.ai/uploads/VIKTOR_interface_for_automation_in_D_foundations_526f7e67c9.png)

*Run the D-Foundations analysis directly through the VIKTOR platform and visualize the result*

## Advantages of binding in D-Foundations

Binding D-Foundations with the VIKTOR platform has various advantages, like: 

- **Single source of truth**: By using the input data from the VIKTOR application, there is always a single source of truth for the analysis. This means no more random data files floating around on different computers or drives. Any changes to these files are logged centrally, so everyone has the most up-to-date data available at all times. 
- **Efficiency**: The binding enables running a great number of analyses in series or even in parallel. In most cases, the whole process of building a D-Foundations model, running the model and then processing the results takes around 5 seconds. This is easily 10 times faster than the average Geotechnical Engineer can setup and run a D-Foundations analysis.
- **Risk reduction**: Once validated, the binding performs all required actions consistently, hence significantly reducing the risk of errors made in the calculations.
- **No extra licenses**: The binding uses the clients own software package and licenses, so the client doesn’t need to purchase additional software or licenses. 
- **Easy to use**: Not only is the binding easy to use for the end-user (who now only has to worry about a handful of input parameters), the engineers and/or developers can write a working script in about 10 lines of code. The binding follows the same structure as the D-Foundations user interface, which is familiar for anyone who has used this software before. This allows engineers/developers to quickly build a user-friendly application, enabling any colleague to perform D-Foundations calculations easily.

## Potential for cost and quantity

These advantages offer a lot of potential for a company, both in terms of costs and quantity:

- The reduction in time allows companies to evaluate more foundation designs and find a more cost-effective design in the same time-frame.
- The reduction in time allows companies to increase their output by taking on additional design projects and completing more projects in the same time-frame.

## Endless possibilities

The binding that has been written for D-Foundations has been set up in such a way that it is as generic as possible. This allows it to be applied to many different varieties in the design of pile foundations. This opens up the possibility of evaluating a great many different designs in an optimization scheme. 

The binding is very user-friendly and follows roughly the same steps as setting up a D-Foundations calculation through the D-Foundations user interface. Therefore, writing the code to create a D-Foundations input file should be very straightforward for an engineer or developer who has used the software package before.

## D-Foundations and VIKTOR

The VIKTOR platform ensures that the data required for the D-Foundations calculations is all centralized in a single place. It also handles all data-traffic between the application and the server with the D-Foundations software package. This way, the Geotechnical Engineer doesn’t even need to have the software or the input data installed on his device to be able to perform D-Foundations calculations and analyses!



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Automate the processing of pile foundations with D-Foundations and Python

### Content: 

1. An introduction to developing successful applications
2. Designing and planning your application
3. How to start coding
4. Gathering and processing feedback
5. Increasing the adoption of your app



How to create successful application that ensure adoption

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How to create a succesful that people will adopt

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Do you want to build a successful application? This guide is here to help you make apps that provide real value to your work and that other people will definitely want to use too!

Over the past years, we, the application developers at VIKTOR, have built many apps for many different industries. We have learned a lot from that and now want to share our experiences with you. 

The principles and processes we explain in this guide are the same as we apply daily. They have helped us build anything from simple apps designed to save time to advanced engineering applications that are used to automatically calculate underground tunnels, dikes, and other large structures. 

This guide will provide actionable insights that are useful for anyone involved in creating custom software tools. For example:

- People developing the tools (developers, engineers, data scientists, etc..)
- End-users, who will use the tools when they are released
- Project managers who are interested in workflow automation/optimization
- Program managers, who oversee and stimulate digital transformation initiatives
- Domain experts, who provide their expertise to people creating software tools
- And anyone else interesting in the process of creating awesome software tools

Learn the process to build apps that provide real value to your work and that other people will definitely want to use as well.

Build successful applications

Learn how to build apps that provide real value to your work and that other people will definitely want to use as well.

Building Successful Applications

## No more copy + paste

Roland Snelders has been involved in the development of the concrete slab reinforcement application for Heijmans. “We could read the internal moments from a SCIA file and these were collected in the report, usually in a table. Then those moments could be entered in Excel and the reinforcement was determined and tested in an iterative process. That calculation was therefore copied to the report. ” This way of working could be done more efficiently. It would be easy if copying numbers is no longer necessary, because everything is collected in one place.

## Excel, SCIA, and VIKTOR

That's exactly what the application they created does. Snelders: “From SCIA we can now export an XML file and read it into the application on the VIKTOR platform. The Excel sheet is also linked to this. After selecting the concrete cross-section data and various reinforcement options, I get a picture with colors that indicate where the reinforcement should be.” This ensures that you go directly from the SCIA file with a once-off data entry to a visualisation of the results that come from the Excel sheet, without having to copy anything.

## Centralized Excel sheets

The Excel sheet has not been completely transferred into the application but rather has been linked to it. This has the advantage that it does not have to be recreated, which would then have to be validated. In addition, all manufacturers know how to make a calculation in Excel, you can still continue to do that. “The application ensures that everyone now works with the same Excel sheet, since it can only be opened centrally,” says Snelders. This significantly reduces the risk of errors.

## Automated calculations

The application can be used in any construction that contains a slab, for example a floor, a wall, a roof or a bridge. “Initially, only rectangular slabs could be calculated and analysed, which was a major limitation,” says Snelders, “This has now been improved to allow for all types of slab shapes to be calculated.” 

This is the beginning in terms of automation. "For the time being it is not going to be possible to calculate entire constructions completely, because there is a lot of variation in every project." Snelders explains, "But it is very useful to have components of such a construction that can be automatically calculated." 

![Customer_case_automating_calculation_process_linking_excel_SCIA_2](https://cms.viktor.ai/uploads/Excel_and_SCIA_connected_c24d123297.png)


## More time for complex challenges

There is less variation in the components themselves, so they can be calculated perfectly with an application. This allows the structural engineer to have more time for the more complex and challenging aspects of their work.

Snelders: “The application makes work more fun. I do not find completing an Excel sheet challenging work. ” When filling in cells and copying data, a structural engineer adds little knowledge to the process. Where they do add knowledge is during the schematisation of the construction and the making of a calculation model, in order to subsequently arrive at an optimal and practical choice of reinforcement. "That work still remains, we have mainly automated the repetitive calculations."

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Automating concrete cross-section calculations by linking Excel sheets to SCIA

Previously, many calculations for fender systems were done with rules of thumb. This is no longer possible, due to the stricter new standards. The calculations now have to be done in more detail, which creates a lot of extra work for the engineers. Calculating all options manually one by one is laborous, so it was decided to automate them. Louis: “The calculation of constructions will be increasingly automated. The time gained from this can then be used to solve complex problems.”

## Automating repetitive tasks

Peltenburg explains: “Repetitive elements are very suitable for programming, and so can be calculated using an application.” With repetitive elements, the same calculation must always be performed, something that an application on the computer can do much faster than a structural engineer by hand. “Once the repetition is found in a construction, letting a computer do that calculation allows one to save a lot of time,” says Peltenburg.

## RFEM and VIKTOR

MH Poly uses the calculation software RFEM to calculate the fender systems. Louis was very interested in creating an application for fender systems and asked Peltenburg to work on this together. The application they created contains an interface between the RFEM software and the VIKTOR platform. Peltenburg: “We offer an API to all our customers, on which applications can be made. This was the first time that it has been taken on in a professional manner.”

![Dlubal and VIKTOR.jpg](/uploads/Dlubal_and_VIKTOR_6fb3708ac2.jpg)

## Automating engineering

In the fall, the application will be presented on the MSc Engineering customer day. “We want to show our customers that the sector is changing and what possibilities there are in terms of automation.” Peltenburg says. Louis, “This application is a pilot to see how it goes, but it is my intention to collaborate more if everything goes well.” 

“The biggest advantage of using VIKTOR is to initiate a change in thinking amongst engineers. There is still a lot of traditional thinking, which has to change to a way that makes better use of ICT developments in the field of engineering”, says Louis. Peltenburg adds, “The nice thing is that you don’t know what will happen in the future. There are so many options it can be used for: [Artificial Intelligence](https://www.viktor.ai/blog/40/artificial-intelligence-machine-learning-engineering-construction), [Parametric Design](https://www.viktor.ai/parametric-design-software), [Machine Learning](https://www.viktor.ai/blog/40/artificial-intelligence-machine-learning-engineering-construction).”

*MH Poly is an engineering company that has been using the computational analysis software RFEM from Dlubal for years. This software is provided by MSc Engineering. Emiel Peltenburg is a specialist at MSc Engineering and distributor of the software RFEM and IDEA StatiCa.*

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Advantages of integrating RFEM calculation software with a cloud application

1. Why the Engineering and Construction industry is falling behind
2. Key components for a successful digital transformation
3. Stages of starting a digital transformation
4. Implementing your first digital transformation
5. What digital transformation means for you

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By 2021, the Engineering and Construction industry is one of the fastest growing sectors. Even though the sector is growing rapidly, it is still amongst the least digitized industries around the world. This is not due to the lack of supply of digital solutions, but rather due to the fact that the adoption of new technologies is not forthcoming within the industry. Finding ways to boost this adoption is both a big challenge and opportunity. Therefore, it is important we start at the right place.

There are three aspects to digital transformation: first people, then process, and after that technology. Adopting new digital solutions in your organization is impossible when your people are not on board with it. That is why convincing your people about the benefits is the first priority in digitally transforming your organization. Instead of looking at the digital solution from a technology perspective, you have to look at it from a people perspective. Your digital solution should never be implemented for digital’s sake. It is there to help the people in your organization do their job easier, quicker, better, and with more pleasure. After they are convinced that it will do just that, digital transformation can start!

In this white paper, it is explained how you can start digitally transforming your organization whilst keeping people at the centre of your digital solution. 



See how you can boost digital transformation in your company in the E&C industry by putting people first and technology second!

Download the White Paper and get INSPIRED

Read all about how you can boost digital transformation for your company in the Engineering and Construction industry by centralizing digital solutions around people, instead of around technology. 

People First, Tech Second: The Way to Boost Digital Transformation in the E&C Industry

## Limitations for project developers

During the process of a feasibility study, project developers face some of these limitations:

- Calculations for the assessment of a project are done up front or after meetings or discussions with stakeholders. This makes it difficult to make decisions during a meeting about the requirements and constraints discussed at the moment.
- Sharing information or project input with colleagues and relevant parties during developments is difficult, since these may vary during the assessment. Keeping track of the most recent input is therefore a challenge.
- Obtaining an optimal result is an almost impossible task, with projects usually having many variables and constraints, and projects looking different from one another.

## Optimization for Real Estate Development

### Advantages of constructing optimizations
As was mentioned, project developers (from now on “project developers” and “the user” will be used interchangeably) are limited by constraints and uncertainty ranges to determine the feasibility of and/or best outcome for the project. The calculations required to determine the outcome for a single set of inputs are usually simple in terms of complexity, and therefore do not require a lot of computational power. Project developers usually also only want to have one optimal outcome, or cost function, which is to obtain the highest earnings for a project. These two characteristics (low computational complexity and a single cost function) make it especially advantageous to construct an optimization. In relatively little time, the user could therefore calculate thousands to millions sets of inputs to get an optimal result.

### Critical assesment

It is therefore up to the user to determine all the inputs needed for a project*, after which should be determined which, with what interval**, and to what extent the input should vary, and what the constraints are. Any optimization has its limitations, and therefore, no matter how simplistic a calculation is, the number of sets of inputs do influence the time of the optimization immensely.

For example, let’s say a project consists of low, medium and high income segments. If the number of units would be used as variable, with every segment’s units ranging from 50 to 100, together with a 1-unit interval (assuming that constraints have been added to the equation), the number of input sets equate to 132,651 (51³)***. By increasing the interval or the number of variables will affect the number of input sets dramatically. It is therefore important for the user to critically assess the variables, and to determine to what extent they affect the outcome.

### Changes to bring to the optimization
There are ways to customize the optimization to obtain an optimal value in the shortest period of time. Here is a list of changes one can bring to the optimization:

- The calculations and logic of the project developer can be expressed in code, and this is no different with the optimization. A project developer with years of experience has a good idea which variables have a greater impact on the outcome. The optimization could therefore be split into different stages. This optimal value is calculated by first finding the optimal input for a certain set of variables with the greatest impact in the first stage, and using the optimal variable set of this first stage for the following stages.
- If these sections can provide an optimal variable set independent of the other, an optimization can be split into different sections.
- If the user has a good idea where the optimal value for a certain variable is, the value could be initialized at that approximated point, and the optimization could be tuned to have a narrow range (and therefore few intervals) around the optimal value.
- The optimization could be split up into iterations, with each iteration having less intervals or a narrower range. After each iteration, the optimal variable set is taken for the next iteration.
If the optimization still does not perform as the user would expect, the user should consider using advanced optimization models (although the models are redundant if the variables show linear tendencies).

## Using VIKTOR as tool
VIKTOR  could play a major role in relieving pressure for the project developer. The VIKTOR platform uses a Python framework, which makes it possible for the user to define all calculations and logic using Python. The variable input can also be defined with a structure with any defaults and exceptions included, making it easier for the user to add input. The user could then use VIKTOR as a tool during discussions or meetings with stakeholders by quickly assessing the requirements and constraints being discussed. This makes it possible to make decisions much quicker on the spot. The ability to compute thousands of scenarios within seconds could further enhance the decision-making process, especially when applying a well set up optimization. VIKTOR’s central data structure also allows for multiple colleagues within the company to have access to a project’s input, therefore eliminating the process of sending the project input to colleagues.

### Sources (only for reference)

[Crowdstreet.com](https://www.crowdstreet.com/resources/risk/development-process-risk/)

[Researchgate.net](https://www.researchgate.net/figure/Potential-stakeholders-for-a-real-estate-development-project-adapted-on-Cleland-1999_fig3_260638592)

** The VIKTOR SDK provides a variety of field options, some which are useful for repetitive inputs. Suppose a development consists of various sections, all with the same type of inputs (e.g. area, cost per square meter, revenue per square meter, etc.). These could be structured using dynamic arrays or tables within VIKTOR.

*** Assuming the optimization requires an interval for a specific variable

**** Assuming the brute force optimization method

Viktor as a tool within the real estate development market

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VIKTOR as a tool within the Real Estate Development market

## Get the white paper

**INDEX**

1. Construction companies and digital transformation
2. Technology trends for a different approach
3. Technology trends in the construction industry
4. A new technology trend for the construction industry
5. Low code for the construction industry
6. The VIKTOR platform
7. What does it cost?
8. How to start
9. What's next?

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Autonomous cars, algorithms that are trading stocks, people having conversations with robots and digital services from the valley make our lives easier. It is already part of many different industries and drives extreme productivity. Considering these benefits of digitalization, there is huge potential in introducing this in the engineering and construction sector.

However, the engineering and construction industry spends way less on digital transformation than other sectors or automotive. So when the benefits are obvious, why have the engineering and construction industry not yet fully embraced new digital technologies? The reasons of lacking behind are understandable and have to do with the characteristics of the industry. But we have come to a time where there are increasing opportunities at hand for 
the sector to speed up digital transformation.

What are these trends in digital transformation and how will it effect the engineering and construction industry? You can read the answers to these questions and more in the white paper.



Learn about the trends on digital transformation and how to take the next step.

What are the trends in digital transformation and how will it effect the engineering and construction industry? You can read the answers to these questions and more in the white paper.

Digital Transformation Engineering and Construction Industry

## Looking for similarities

By looking closely at the similarities between the companies, the collective came up with an application that automates the basic knowledge. Paulus Eckhardt, Director Design & Engineering, of Ballast Nedam: “We mainly work on the fundamental levels of the process, that is not competitive. This is where the Product Owners of the companies meet.” The application makes it possible to quickly calculate a large number of cross sections for soil bodies by automating settlement calculations (D-Settlement). “With this we can realize a shorter design time, we can implement changes faster and do better calculations. In this way we reduce the risk of errors and we ensure lower failure costs”, says Jos van Rijen, Manager Design Operations, BAM.

## Using the knowledge

The role of people in projects is also changing. “The changes in the labour market require knowledge and experience to be safeguarded, while the challenges in terms of integrality and sustainability are growing,” says Eckhardt. He is referring to the slow dissappearance of very experienced engineers with a lot of knowledge. The VIKTOR platform offers a solution because it is precisely this knowledge that is bundled and further developed. This application is the first of three applications to be developed with the Digital Engineering Community (DEC), which offers builders the opportunity to automate their engineering together with the help of programming engineers from VIKTOR. Costs are shared by investing in the application and its development together. Coen Sanderink, Heijmans Business Developer Digital Infra says: “The market is being automated, and this is therefore a good way to develop faster and cheaper. We can then use our budget more effectively and work faster.” As a result, the cooperating contractors can continue to distinguish themselves in other areas.

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Unique collaboration between BAM, Ballast Nedam and Heijmans in DEC


>“We are very enthusiastic about this collaboration! Together with our business ProductOwner and a ‘Coding Engineer’ we are exploring this collaboration in the development of the underwater concrete floors application. Quite exciting, with questions as ‘what is generic and suited for shared development?’ versus ‘where do we secure our own IP?’ It is clear that this kind of automation is necessary. The exploration surrounds the question: What can we do together? And with that faster and cheaper?” – Mark van den Brink, Digital Transformation Lead at WSP 

## From idea to proposal
 
In a few sessions with the participating parties, the four ideas have been worked out into the following proposals:

### Crane hardstand

The purpose of this development is enabling engineers to determine the stability of crawler- and outrigger cranes, including the design of possible needed ground improvement, fast and easy to decrease the changes of tripping over. Within the application, cranes can be inserted parametrically, from which the maximal ground pressure can be determined. CPT’s (.gef files) can easily be uploaded as well. These are directly interpreted to a schematized overview of the soil layout. From there on, the stability can be determined through an integration with D-Foundations (shallow foundations). 

### Floor on piles

Many infrastructural objects consist of a concrete foundation- and floor. With this development, an optimal design of the combination floor and piles can easily be generated, including the ideal reinforcement configuration. A constructor can use this application to easily link the force effect from a finite element model (e.g. SCIA) to a cut test (e.g. IDEA StatiCa). Additionally, the pile plan can be varied to find the optimal configuration between plate reinforcement and piles. 

### Sheet piles/Construction pits

To realize construction and (temporarily) turn ground, we use sheet piles and construction pits. The purpose of this development is to parametrically design sheet piles and integrate these with anchors, underwater concrete floors, outrigger frames, etc. Within this application, the sheet pile is tested through an integration with D-SheetPiling, which enables the fast calculation of different cuts and variants.

### Underwater concrete floors

Underwater concrete is, amongst others, used for underground parking garages, underpasses, and tunnels. This development fits nicely with sheet piles/construction pits. With the application, a design of a floor including anchorage is parametrically designed to automatically generate the optimal configuration. The underwater concrete floors are tested conform the CUR77. The carrying capacity of the anchors/piles is determined through an integration with D-Foundations. 

## How to participate? 

To turn the development ideas into concrete proposals, a few in-depth sessions were organized to come up with ‘user stories’ that form the basis of every application’s functionality. Based on the user stories, a development plan and associated estimation of the costs can be determined. In March, the first definitive proposals will be sent to the entire community. Every party that is interested in joining one or more of the development proposals can sign up.

>“It’s like the old saying “many hands make light work”. Especially with crane hardstands. We all have the same goal in mind, which is safety. This is something we should take on together, so that we can quickly develop a good tool that helps us check the stability of cranes.” – Arie-Jan Tol, Team Lead at engineering firm Hektec
 
## What is the DEC?

*Accelerated digitization through collaboration*

The DEC is thé digital meeting point for the construction and infra sector, where ideas for software development come together, are started up, and accelerated. 

We share our knowledge so that we can work together to invent the wheel, make it easier to finance the (engineering) process, and offer smart and high-quality solutions. 

The initiators are Ballast Nedam, BAM, Boskalis Nederland, Dura Vermeer, Heijmans, Mobilis, Van Oord, VIKTOR, VolkerWessels. Antea Group, CRUX, Hektec, RPS, Tauw, and WSP are now a part of the DEC as well.


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The Digital Engineering Community is expanding and has 4 new development proposals for engineering automation apps early 2022. Join now!

4 development proposals for digital engineering community early 2022

Expansion DEC brings 4 development proposals early 2022

VIKTOR Blog